1. Field of the Invention
The present invention relates to traffic generation for network analysis and network testing.
2. Description of Related Art
Multiple recent studies of high-speed Ethernet, ATM, Local-Area Networks (LAN), Wide-Area Networks (WAN), Storage Area Networks (SAN) I/O traffic, signaling, WWW, multimedia and video traffic have demonstrated that the variability in typical network traffic involves non-negligible correlations across several time-scales. These evaluations challenge traditional data traffic modeling, traditionally based on the Poisson process and other Short-Range Dependent (SRD) processes.
One of the most striking features of packet-switched network traffic is its tremendous burstiness, persistent at practically any time scale. Such Long-Range Dependence (LRD) manifests itself through a self-similar or fractal-like behavior. “Self-similarity” means that a segment of traffic measured at one time scale resembles an appropriately scaled version of the traffic at a different time scale.
Many networking studies have considered self-similarity, both for analysis and synthesis of the fractal characteristics of network traffic. The communications industry (e.g., AT&T, Nortel, Ericsson) has been supportive of research groups in this area, and more recently some companies have started developing self-similar traffic generators to measure and test networking equipment, as well as properly scale it during system design.
Fractal phenomena are common in both natural and human-made scenarios, including natural landscapes, ocean waves, earthquake distributions, stock market behavior, and packet-network traffic. As used herein, fractal and self-similar behavior are considered synonyms.
It has been proven that heavy tails in flow sizes (or lengths) are able to generate self-similarity. Heavy-tail distributions are those whose tails decay with a power law (which is a much slower decay than exponential), indicating non-negligible probability even for extremely large observations. They describe long-memory processes with robust time dependence configurations that vanish very slowly. The “heavy-tailedness” of a random variable puts in evidence the combination of numerous small observations mixed with a few large observations, where most of the contributions to the sample mean and variance of the dataset comes from the few large observations.
Important work in this field has been done by Leland, Taqqu, Willinger and Wilson. See, for example, W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, “A Bibliographical Guide to Self-Similar Traffic and Performance Modeling for Modern High-Speed Networks,” in Stochastic Networks, F. P. Kelly, S. Zachary, and I. Zieldins (eds.), Oxford University Press, pp. 339-366, 1996; and W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, “On the self-similar nature of Ethernet traffic,” IEEE/ACM Transactions on Networking, vol. 2, pp. 1-14, February 1994. Other important work in the field has been done See for, example, B. K. Ryu, “Fractal Network Traffic: From Understanding to ions,” Ph.D. Thesis, Columbia University, NY, 1996.